Data Science Ethics Book Club #15

Our next data science ethics book club is on Ethics of AI and Personal Insurance. Artificial intelligence (AI) and machine learning (ML) are increasingly used in the personal insurance industry, including customer onboarding, pricing, and fraud detection. While this offers a number of benefits, such as improved customer engagement and lower prices, there are ethical concerns around consumer privacy, hyper-personalisation of insurance, and potential unfair biases.

This book club session will aim to understand some of the pros and cons of the use of AI in this industry, identify the ethical issues, and discuss potential ways forward in which insurers can address and mitigate them.

Reading Material

You are welcome to pick from this reading list, depending on your interest and the time you have:

1. UK CDEI Snapshot Paper https://www.gov.uk/government/publications/cdei-publishes-its-first-series-of-three-snapshot-papers-ethical-issues-in-ai/snapshot-paper-ai-and-personal-insurance or https://apo.org.au/sites/default/files/resource-files/2019-09/apo-nid267086.pdf
2. Research paper, “Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing”
3. Research paper, “The Fairness of Machine Learning in Insurance: New Rags for an Old Man?”
4. European Insurance and Occupational Pensions Authority (EIOPA) paper, “Artificial intelligence governance principles: towards ethical and trustworthy artificial intelligence in the European insurance sector”
5. Chartered Insurance Institute (CII)
6. Blog post, “How AI Is Transforming the Insurance Industry [6 Use Cases]”

Questions and provocations

• What are the potential benefits of the use of AI in insurance?
• What types of data do you think are “off-limits” for insurance pricing, and why? (E.g. social media data)
• New algorithmic approaches could result in some people being priced out of insurance, as risk assessments become more precise and previously unseen indicators of risk are revealed for the first time. What are the risks of this, and how do we address them?
• Should insurers be allowed to suggest lifestyle improvements to their customers?
• How can we protect customers from unfairly biased algorithms?

All welcome!

And if you'd like to facilitate one of the groups (usually 5-8 people), we'd love to have you! Please email Contact@datakind.org.uk about this.

As this is an in-person event, we are limited to 80 spaces. Please let us know if you can't make it so we can open up a slot for someone else.

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*FAQs*

Why are we doing this?

There's a lot of good writing out there and one of the most important principles (even in our own ethical principles) is to discuss and debate the ethical questions.

We hope this will help people gain the tools they need to think about this in their jobs or in DataKind projects, or in encountering algorithmic tools in their everyday life. But if all you get out of it is some friendly discussion over a coffee or beer, that works too :-)

Do I need to be a data scientist to participate?

Nope. We'll have a mix of technical and non-technical reading material. The aim is to think about data science in a context of ethical impacts and consequences - and that affects everybody!

I have a brilliant idea for reading material/a theme! Who do I tell?

We love suggestions! Tell us at contact@datakind.org.uk